Which will help your business be more successful: statistics or probability?
Underwriters at insurance companies use statistics to assess future risks. This is based on years of collected data.
Probability is what card counters in Vegas use to increase their odds of success. This is based on real-time, real-life experience.
If you want to play it safe, use statistics. If you want to win big, use probability.
There Are Lies, Damned Lies, and Statistics – Mark Twain
Businesses are increasingly using statistics to manage decision making, as evidenced by popular books like Tom Davenport’s Competing on Analytics and the boom in CRM system usage.
The belief is that if we gather more data we can make better decisions. But this may not be true when it comes to innovation.
If you are crunching numbers, you are probably gathering information from existing customers. This will give you insight into their buying habits, usability behaviors, and other patterns. But most likely you are only gathering data on YOUR customers. This represents the middle of the bell curve or the norm. This information may be useful in “incremental” improvement, but it will rarely lead to significant innovations.
When you move beyond the norm to the far ends of the bell curve, you will find the real interesting ideas.
Being normal is not a virtue; it denotes a lack of courage
On the far right-hand side of the curve are the market leaders; the advanced users. They may not be your customers because you can’t meet their high-end needs. Or maybe they were once your customers and they left. When someone is not a customer it is difficult to gain insights into their wants and needs. If you could somehow understand their perspectives, you may find opportunities for “advanced” innovation and insights on where the industry may be going in the near future. These innovations would be more radical, yet continuous in nature. Think of this as the Blu-ray improvement on the standard DVD (we’ll save a discussion on the demise of HD DVD for another time).
On the far left-hand side of the curve are the laggards; the less sophisticated users. Your products/services may be too advanced, too complicated, or too expensive for their needs. Again, you are probably not gathering statistics on these individuals or organizations. But here lies the greatest opportunity for discontinuous innovation. Or as Clayton Christensen would call it, disruptive innovation. If you can find a way of “dumbing down” your offering, you might find new and untapped sources of revenue. Quite often these products become the de facto standard, much like when PCs replaced the more sophisticated mainframes and mini-computers.
The problem is, it is very difficult to get data about the ends of the bell curve. Focus groups, surveys, and other traditional data gathering techniques are useless. I love this quote from Scott Cook at Intuit: “For every one of our failures, we had spreadsheets that looked awesome.” We can use numbers to justify anything we want. But quite often they justify the wrong actions.
The Probable is What Usually Happens – Aristotle
If a statistics-driven innovation model does not work, what would a probability-based model look? Probability tells me that if everything is equal, the more bets I have, the more likely one will be successful. The odds of 1 success out of 200 are greater than 1 success out of 20.
But how can you have more bets without diluting your effort and potential returns? The key is to learn as you go. This is exactly what card counters to.
Let’s contrast a more statistics-driven model with a probability-based model. To do so, we will use two exceedingly simplistic examples. With innovation model #1, you make a few “big bets” based on analytics you gathered from your customers (a statistics-driven model). Innovation model #2 is a more experiential “learn as you go” model (a probability-based model).
In both examples, let’s assume you have $100 million to bet, woops, I mean invest in innovation.
Innovation Model #1 – Big Bets: This is the most common approach and is highly driven by statistics. You identify a number of large innovations you want to invest in. For this example we’ll use 20 projects @ $5 million each. No matter how much data you have, most innovations will fail. And of the successes, most will not achieve the predicted ROI. In the end, if you are lucky, you’ll have 3 wins out of 20. This feels like putting all of your money on 35 black on the roulette table and crossing your fingers. Your successes/wins had better pay out big to cover your losses.
Innovation Model #2 – Learn As You Go: Let’s look at a different model. What if instead of 20 large projects, you have 200 smaller projects. Again, you know that most of these will fail – but you don’t yet know which ones. You initially invest a small amount ($10M or $50K per project) to test the ideas as low-risk, low-cost experiments. Based on this experience, you decide that 40% (80) of the ideas still show some promise. But you are not yet ready to bet the house. This time you allocate an addition $20M ($250K each) to do further testing. You now eliminate 70% of the projects, keeping 24 alive. You now invest another $20M (nearly $1M per project). Of these 24, you decide that 5 are real winners. At this point you have only spent half of your money and yet you were able to eliminate 195 ideas. That’s incredibly valuable information learned by doing rather than by analyzing. You now invest the remaining $50M on those 5 ($10M each).
With innovation model #1 you must “guess” which ideas will be successful up front. HP is moving to this model by consolidating 150 ventures into only 20. This feels like a bad bet. What if the 20 they chose all turn out to be duds and that the real winners were in the 130 they eliminated?
With innovation model #2, you make lots of small bets. And as the odds of success improve, you increase your bets. This is the business version of card counting. As you get to the end of the deck, you have “real life” information that guides your bets. As the odds of success increase, you increase your bets. This method works. Just ask the kids from MIT who won millions card counting in Vegas and will make millions more with their new movie, 21.